Essays on the ECB Monetary Policy's Impact on Non-Financial Firms

Essays on the ECB Monetary Policy's Impact on Non-Financial Firms

Lior Cohen

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PhD in Economics

PhD in EconomicsLior Cohen

Lior Cohen

6bbPhb^] the ECB Monetary Policy's Impact on Non-Financial Firms

2020

PhD in Economics

Thesis title:

Essays on the ECB Monetary Policy's Impact on Non-Financial Firms

PhD student:

Lior Cohen

Advisors:

Marta Gómez-Puig Simón Sosvilla-Rivero

Date:

March 2020

v To my Family, from both sides of the Atlantic

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Acknowledgments

I would like to express my gratitude to my family, Idan, Eytan, Oren, Maureen, Elan, Sam, Revital, and Mazal, for their love, I would like to thank my advisors Marta Gómez-Puig and Simón Sosvilla-Rivero for their guidance and support along the way, I would also like to thank Elisabet Viladecans-Marsal and Jordi Roca Solanelles for their support, a shout-out to my friends, Yohay Elam, Robert Weisz, Zemer Avital, Eran Dolev, Asaf Stein, Nadan Tsur, and Lucía Santos Collantes, for being there for me, and my utmost gratitude goes to Itai Furman for his tireless efforts in helping me with this thesis – thank you buddy!

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Table of Contents

Chapter 1 – Introduction ... 1

1.1 Motivation ...1

1.2 Summary of the thesis ...4

1.3 Structure of the thesis ...7

Chapter 2 – Has ECB’s monetary policy prompted NFCs to invest, or pay dividends? ... 9

2.1 Introduction ...9

2.2 The effects of ECB’s monetary policies on NFCs ... 10

2.3 Analytical framework ... 11

2.3.2 Capital structure ... 11

2.3.3 Capital spending, dividends, and buybacks ... 12

2.4 Data ... 16

2.5 Econometric estimation ... 19

2.5.1 Leverage ... 19

2.5.2 Capital spending and shareholder’s yield ... 21

2.6 Empirical results ... 23

2.6.1 Panel unit root tests ... 24

2.6.2 Leverage: Empirical results ... 25

2.6.3 Capital spending: Empirical results ... 26

2.6.4 Shareholder yield: Empirical results ... 28

2.6.5 A cross-country analysis... 30

2.6.6 A cross-industry analysis ... 31

2.7 Concluding remarks ... 31

Appendix A: Description of variables and data sources ... 33

Appendix B ... 34

Chapter 3 – Examining the effect of ECB monetary policy on non-financial corporations’ credit risk premia ... 41

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3.1 Introduction ... 41

3.2 A brief review of the ECB’s monetary policy effect on the bond market 45 3.3 Data and descriptive statistics ... 48

3.4 Econometric strategy ... 49

3.4.1 Daily data analysis ... 49

3.4.2 Monthly data analysis... 51

3.5 Empirical results ... 52

3.5.1 Daily data analysis results ... 53

3.5.1.1 An OLS regression analysis ... 54

3.5.1.2 A Bayesian regression analysis ... 55

3.5.1.3 An alternative event study for the announcements ... 57

3.5.2 Monthly data analysis results... 59

3.5.2.1 Baseline model ... 61

3.5.2.2 The ECB and the Federal Reserve ... 63

3.5.2.3 Asset purchase programmes ... 65

3.5.2.4 MRO, LTRO, and TLTRO ... 67

3.5.2.5 A cross-country analysis ... 69

3.5.2.6 Market capitalization ... 71

3.5.2.7 Credit rating ... 72

3.6 Concluding remarks ... 73

Appendix C: List of the ECB’s policy announcements ... 75

Appendix D: Description of variables and data sources ... 76

Appendix E: List of the ECB’s QE programmes ... 77

Chapter 4 – Bang for the QE buck: Examining the impact of ECB’s corporate bond purchases on firms’ credit risk, debt and investment ... 79

4.1 Introduction ... 79

4.2 The effect of corporate purchase programmes – related literature ... 81

4.3 Data and descriptive statistics ... 84

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4.4 Econometric methodology ... 90

4.4.1 Bonds and cost of debt ... 93

4.4.2 Impact on corporate decisions ... 94

4.5 Empirical results ... 96

4.5.1 Credit risk channel ... 96

4.5.2 Liquidity risk channel...101

4.5.3 Corporate decisions ...101

4.6 Concluding remarks ... 105

Appendix F: The CSPP’s outline ... 107

Appendix G: Description of variables and data sources ... 108

Appendix H ... 109

Appendix I ... 110

Chapter 5 – Concluding remarks ...113

References ...117

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List of Figures

Figure 1.1: Contributions of sectors to the growth of nominal gross capital formation in the Euro Area 2009-2019Q3 ... 2 Figure 1.2: Investment rate (percent) of European Union non-financial firms 2006-2019Q3 ... 3 Figure 2.1: Euro Area investment and 10- year European Union (EU) yield, quarterly data, 1999-2017 ... 13 Figure 2.2: Capital formation in selected EMU countries and capital spending of firms in the sample, 2001-2016 ... 17 Figure 2.3: Private debt in selected EMU countries and total debt of firms in the sample, 2001-2016 ... 18 Figure 3.1: 10-year government bond yields for selected EMU countries, 2000-2018 ... 41 Figure 3.2: Spread between the cost of borrowing for corporations and the ECB’s deposit rate in the Euro Area, Germany, France, Italy and Spain 2003- 2018 ... 42 Figure 4.1: Spread of 5 yr. Europe’s corporate investment-grade over 5 yr.

generic German bunds 2013-2019 ... 82 Figure 4.2: Average G-spread (GS) and Z-spread (ZS) of targeted bonds and non-targeted bond by the CSPP 2015-2019 (basis points) ... 86 Figure 4.3: Average scaled bid-ask spread for targeted and non-targeted bonds, 2015-2019 ... 87 Figure 4.4: Cost of debt of targeted and control groups in the sample (percentage point), 2014Q1-2019Q3... 89 Figure 4.5: Debt levels of targeted and control groups in the sample (millions of Euros), 2014Q1-2019Q3 ... 90

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List of Tables

Table 2.1: Results of panel analysis for debt-to-equity ... 25

Table 2.2 : Results of panel analysis for capex-to-sales ... 27

Table 2.3 : Results of panel analysis for shareholder yield ... 29

Table B2.1 : Tests for unit roots ... 34

Table B2.2 : Results of panel analysis for the debt-to-equity equation by countries ... 35

Table B2.3 : Results of panel analysis for the capital expenditures equation for countries ... 36

Table B2.4: Results of panel analysis for the shareholder yield equation for countries ... 37

Table B2.5: Sectorial results of panel analysis for the debt-to-equity equation ... 38

Table B2.6: Sectorial results of panel analysis for the capital expenditures equation ... 39

Table B2.7: Sectorial results of panel analysis for the shareholder yield equation ... 40

Table 3.1: The ECB’s policy announcements, event-style analysis, day of the announcements, June 2, 2014-Dec. 30, 2016 ... 54

Table 3.2: Bayesian regression analysis, day of the announcements, June 2, 2014-Dec. 30, 2016 ... 56

Table 3.3: Credible intervals of the Bayesian regression analysis, day of the announcements, June 2, 2014-Dec. 30, 2016 ... 57

Table 3.4: The ECB’s policy announcements, event style analysis, five days before and after the day of the announcements, June 2, 2014-Dec. 30, 2016 ... 58

Table 3.5 : Baseline model – a regression analysis on the effects of the ECB’s policies, Jan. 2008- Feb. 2018 ... 62

Table 3.6: Regression analysis on the effects of the ECB’s policies, controlling for the Federal Reserve’s policies, Jan. 2008- Feb. 2018... 64

Table 3.7: Regression analysis on the effects of the ECB’s policies, breaking down the various QE programmes, and controlling for country of origin, Jan. 2015- Feb. 2018 ... 66

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Table 3.8: Regression analysis on the effects of the ECB’s policies, breaking down the LTRO/TLTRO, and MRO programmes, Jan. 2009- Dec. 2014.. 67 Table 3.9: Regression analysis on the effects of the ECB’s policies, focusing on the TLTRO programme, Jan. 2015- Feb. 2018 ... 68 Table 3.10: Regression analysis on the effects of the ECB’s policies, breaking down the LTRO/TLTRO, and MRO programmes, and controlling for country of origin, Jan. 2008- Dec. 2012 ... 70 Table 4.1: Summary statistics of main variables ... 88 Table 4.2: Results for the G-spread and Z-spread daily regressions analysis ... 97 Table 4.3 Results for the G-spread, Z-spread, bid-ask spread monthly regressions analysis ... 99 Table 4.4: Main results for selected corporate variables regressions analysis ... 102 Table 4.5: Main results for selected corporate ratios regressions analysis 103 Table H4.1: Main results for selected corporate variables regressions short- term analysis ... 109 Table H4.2: Main results for selected corporate ratios regressions short-term analysis... 109 Table I4.1: Main results for selected corporate variables regressions analysis ... 110 Table I4.2: Main results for selected corporate ratios regressions analysis ... 111

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Chapter 1 – Introduction

1.1 Motivation

In recent years, one of the main problems the European Economic and Monetary Union (EMU) has been facing is slow economic growth stemming, in part, from subdued investments despite interest rates falling below the zero-lower bound (ZLB). Summers (2013) brought back the term “secular stagnation” – first coined by Hansen (1939) – to describe the United States’

economic environment following the 2008-2009 Great Recession, in which a central bank is unable to reduce interest rates enough to stimulate investment and consumption. In recent years, another term, “liquidity trap”, has also gained popularity to characterize an economy where short-term interest rates are at the ZLB, and in effect, rendering conventional monetary policy incapable of stimulating growth. Indeed, this topic has fostered extensive research on ways unconventional monetary policies could stimulate an economy (see, for example, Dominguez et al. (1998), Bernanke et al. (2004), and Eggertsson and Krugman (2012)).

The European Central Bank (ECB) has been trying to ameliorate financial conditions and restore confidence in the EMU, especially after the 2011-2012 Euro Debt crisis. On July 26^th, 2012 the then President of the ECB, Mario Draghi, stated the most important three words ever uttered by a central banker that he was going to do “whatever it takes” to save the Euro. Since then, the ECB has introduced an array of conventional and unconventional monetary policies to maintain the EMU project. Some of these policies include slashing interest rates below the ZLB, implementing the longer-term refinancing operations (LTRO), and targeted longer-term refinancing operations (TLTRO), and introducing quantitative easing (QE).

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However, were these policies successful in encouraging investment and easing financial conditions? In this thesis, we try to answer this question from the perspective of non-financial firms.

The analysis of the ECB’s unconventional policies – mainly of QE – has been widely researched, especially their effect on borrowing costs in general and government bond yields in particular (see Albu et al. (2014), De Santis (2020), Jäger and Grigoriadis (2017), and Krishnamurthy et al. (2017), among others). However, the research on corporations has been somewhat limited, although non-financial corporations (NFCs) are a vital sector, particularly for investments.

Figure 1 demonstrates the importance of NFCs to investment growth in the Euro Area. The figure breaks down the contribution of each sector to investment growth. As seen, over the past decade, the NFC sector’s contribution to the Euro Area’s investment growth has accounted, on average, for 67% of its entire investment growth rate.

Figure 1.1: Contributions of sectors to the growth of nominal gross capital formation in the Euro Area 2009-2019Q3

Source of data: Eurostat; data are for the average annual growth rate of each sector in the Euro Area.

Nonetheless, the recovery of NFCs’ investment has been slow since 2009 and has yet to settle at the levels it had reached in 2007. Before the global

-20 -15 -10 -5 0 5 10

Households Non-financial corporations Financial corporations Government

3 recession, NFCs’ average investment rate was 24% (2006-2007); by 2009, this rate declined to 21%; since then, NFCs’ investment rate has been rising, albeit at a slow pace. Only in 2017, the NFC sector reached, briefly, its investment rate from before 2009¹.

Figure 1.2: Investment rate (percent) of European Union non-financial firms 2006- 2019Q3

Source of data: Eurostat. The investment rate is the gross fixed capital formation as a percent of gross value added of non-financial firms

In this thesis, we focus on the ECB’s interest rate policy and its QE programmes, especially the public sector purchase programme (PSPP), and the corporate sector purchase programme (CSPP).

The PSPP, first introduced on January 22^nd, 2015, aimed to lower long-term sovereign bond yields by purchasing sovereign debt at an average pace of 47

1 One reason for this lackluster recovery in the EMU is that, unlike in the U.S., the EMU countries are not part of a fiscal union, they are only part of a monetary union. During the recovery, not all governments provided a fiscal stimulus along with the accommodating monetary policy. As such, the ECB’s efforts may have been less effective than the stimuli implemented by say, the Federal Reserve, where its policies were accompanied, back in 2009, with a fiscal stimulus package to support the economy. Of course, other factors could have played a significant role in the EMU’s slow recovery, such as the Greek debt crisis, lackluster corporate innovation, and more.

20 21 21 22 22 23 23 24 24 25 25

2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1 2013Q3 2014Q1 2014Q3 2015Q1 2015Q3 2016Q1 2016Q3 2017Q1 2017Q3 2018Q1 2018Q3 2019Q1 2019Q3

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billion euros a month from March 2015 to December 2018². In total, the ECB purchased over 2.2 trillion euros worth of government bonds of EMU countries. This asset purchase programme accounted for 47% of ECB’s balance sheet.

Another vital purchase programme was the CSPP. Under this program, the ECB purchased NFC debt at a monthly pace of 5.8 billion euros from June 2016 to December 2018 for a total of 178-billion-euro worth of European corporate bonds. This programme’s goal was to lower NFCs’ borrowing costs and to induce corporate borrowing and investment spending.

While the ECB has been implementing several other monetary policies, including the securities markets programme (SMP), LTRO, TLTRO, forward guidance, etc., the ECB’s asset purchase programmes have been the primary tools the ECB utilized as interest rates reached the ZLB.

1.2 Summary of the thesis

This thesis consists of three independent chapters, albeit with an overarching theme of investigating the impact of ECB’s policies on NFCs.

In Chapter 2, titled Has ECB’s monetary policy prompted NFCs to invest, or pay dividends?, we take a broad view of the influence of the ECB’s conventional and unconventional policies on NFCs’ decisions on debt holdings, investments, and dividends. Toward this end, we use a unique dataset comprised of income statements and balance sheets of leading NFCs’

2 Initially, the ECB targeted purchasing 60 billion euros per month from March 2015 to March 2016 of all three QE programmes, PSPP, asset-backed securities purchase programme (ABSPP) and third covered bond purchase programme (CBPP3); the ECB then augmented the purchasing pace to 80 billion euros in March 2016, before lowering this pace back to 60 billion by April 2017. However, the total purchases under the PSPP were, on average, 50 billion euros until March 2016, and nearly 70 billion euros from April 2016-March 2017. This rate fell back to 50 billion euros for the rest of 2017. By 2018 the average purchase pace was 20 billion euros per month.

5 operating in the EMU from the four largest economies, Germany, France, Italy, and Spain.

Chapter 2 contributes to the literature by shedding light on the ECB monetary policies’ long-term effect on NFCs’ leverage and capital allocation – subjects that, to the best of our knowledge, have yet to be methodically investigated over such an extended period and encompasses the ECB’s unconventional policies.

The main results in Chapter 2 suggest that the ECB’s monetary policies have encouraged firms to raise their debt burden, especially after the global recession of 2008. The ECB’s policies, particularly after 2011, also seem to have led NFCs to allocate more resources not only to capital spending but also to shareholder distribution.

Chapter 3, titled Examining the effect of ECB monetary policy on non- financial corporations’ credit risk premia examines the usefulness of the ECB’s policies in ameliorating financial conditions and reducing the risk premia of NFCs. We collected daily credit default swaps (CDSs) prices of publicly-traded European NFCs to analyze the short-term effects of the policy announcements between June 2nd, 2014, and December 30th, 2016.

We also test the long-term impact of the ECB’s policies on NFCs’ CDS prices using monthly data from January 2008 to February 2018.

Chapter 3 contributes to the literature by being the first to methodically investigate the mechanism of the ECB’s monetary policy’s short-term and long-term impact on NFCs’ CDS prices. By doing so, we assess the ECB’s various policies’ transmission mechanism to NFCs’ risk premia – a critical factor in NFCs’ borrowing costs.

The main findings in Chapter 3 are that the ECB’s asset purchase programme announcements seem to have an immediate impact on CDS daily prices;

these announcements had a stronger effect, especially after the PSPP started in March 2015. From 2008 to 2012 and from 2015 to 2018, the ECB’s interest

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rate policy had statistically and economically significant effects in reducing CDS prices. We also find that some of ECB’s asset purchase programmes, such as the PSPP, had a statistically significant long-term impact on CDSs.

These findings indicate that some of the ECB’s policies were effective in reducing NFCs’ risk premia, notably since 2015, as market conditions improved.

In Chapter 4, titled Bang for the QE buck: Examining the impact of ECB’s corporate bond purchases on firms’ credit risk, debt and investment, we focus on the CSPP. This programme, first announced in March 2016 and started by June 2016, aimed to ameliorate corporations’ financial conditions and encourage NFCs to borrow and invest.

Chapter 4 analyzes the CSPP’s short-term and long-term effect on corporate credit risk by utilizing daily (from March to August 2016) and monthly data (June 2016- December 2018) of corporate zero-volatility, and nominal spreads. We also employ NFCs’ debt covenants data to assess the pass- through of the CSPP to firms’ risk of credit. We examine the CSPP’s long- term effect on liquidity risk by using scaled bid-ask spread data. The data include purchased bonds under the CSPP (targeted bonds) and European bonds that were not purchased. We then analyze the CSPP’s short-term and long-term impact on capital structure and capital allocation of NFCs whose bonds the ECB purchased (targeted firms) compare to European firms whose bonds were not purchased.

Chapter 4 contributes to the literature by shedding light on the CSPP’s short- term and long-term effect on corporate bonds’ risk premia liquidity costs.

Third, to the best of our knowledge, we are also the first to investigate the CSPP’s long-term impact on firms’ borrowing costs and corporate decisions.

In Chapter 4 we find that following the CSPP announcement, targeted corporate bonds’ zero-volatility spread, and nominal spread fell by 3.5 basis points (2.6%) and 4.1 basis points (4.2%), respectively. Initially, the programme encouraged firms to borrow more and pay dividends; however,

7 it did not improve investments. Throughout its implementation (June 2016- December 2018), the CSPP only marginally reduced targeted bonds’ risk premia and did not lower corporate bonds’ liquidity risk. Nonetheless, it reduced targeted firms’ cost of debt, improved their debt covenants, and encouraged investments.

The findings in Chapter 4 suggest the CSPP did not have a persistent impact in reducing credit risk or liquidity risk in the corporate bond market;

however, it had an economically significant lasting effect in lowering corporate debt cost and stimulating investment.

1.3 Structure of the thesis

The rest of the thesis is organized as follows: In Chapter 2, we discuss the impact of the ECB’s policies on investment, dividends, and debt. In Chapter 3, we examine the impact of monetary policies on NFCs’ credit default swaps. In Chapter 4, we focus on the CSPP and analyze its effect on corporate bonds spreads, investments, and debt. Finally, in Chapter 5, we make several concluding remarks.

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Chapter 2 – Has ECB’s monetary policy prompted NFCs to invest, or pay dividends?

2.1 Introduction

This chapter aims to examine whether European Central Bank (ECB)’s conventional and unconventional monetary policies in times of crisis influenced non-financial firms’ decisions. Specifically, it focuses on three critical issues: leverage, investments, and shareholder distribution. The contribution of this chapter to the existing literature is twofold. First, it examines how ECB monetary policies in times of crisis have affected non- financial firms’ decisions on leverage. Second, it analyzes how those policies have influenced non-financial firms’ decisions on capital allocation – primarily capital spending and shareholder distribution (which comprises dividends and share buybacks). To the best of our knowledge, this is the first attempt to delve so deeply into the study of the effects of the ECB’s policies on non-financial firms. To this end, we use an exhaustive and unique dataset comprised of income statements and balance sheets of the leading non- financial firms that operate in European Economic and Monetary Union (EMU) countries.

The main results suggest that the ECB’s conventional and unconventional policies encouraged firms to raise their debt burden, especially after the global recession of 2008.

________________________________________

A joint work with Prof. Marta Gómez-Puig and Prof. Simón Sosvilla-Rivero based on this chapter has been published as: Cohen, L., Gómez-Puig, M., and Sosvilla-Rivero, S. (2019). Has the ECB’s monetary policy prompted companies to invest, or pay dividends? Applied Economics. 51: 4920- 4938.

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Moreover, the ECB’s monetary policies – mainly after 2011 in the wake of the European economic crisis and with the appointment of Mario Draghi as president – also seem to have led non-financial corporations (NFCs) to allocate more resources not only to capital spending but also to shareholder distribution.

The rest of the chapter is organized as follows. Section 2 reviews the literature on the effects of the ECB’s monetary policies on non-financial firms. Section 3 presents the analytical framework. Section 4 describes the data used in the chapter. Section 5 explains the econometric methodology, and Section 6 reports the empirical results. Finally, Section 7 presents the concluding remarks and suggests some possible policy implications.

2.2 The effects of ECB’s monetary policies on NFCs

An extensive literature has studied the impact of ECB’s policies since 2011 from different perspectives and using different methodologies; however, only a few papers have focused on the effects of these policies on non-financial corporations, despite the crucial role that the latter play in the economy³. Lenza et al. (2010) and Giannone et al. (2012) focus on the impact of the ECB’s monetary policy on macroeconomic variables by applying vector autoregression (VAR) methods, while Gambacorta et al. (2014) examine the relations between the ECB’s balance sheet and macroeconomic conditions.

They estimate a panel of eight advanced economies and show that an unexpected rise in a central bank’s balance sheet – mostly via quantitative easing (QE) – would raise liquidity (supply side), especially in countries where central banks are already hitting the zero-lower bound and under the prevailing conditions following the global economic crisis of 2008.

3 According to Eurostat, non-financial firms account for nearly 58% of the total gross added value in the Euro Area and 55% of Euro Area’s gross fixed capital formation (2002-2017 average).

11 Indeed, only a few papers have attempted to show the link between non- financial corporations’ investments in the EMU and the ECB’s monetary policy. Darracq-Paries and De Santis (2015), who look at the effects of the 3-year long-term refinancing operations (LTROs) by considering them as a credit supply shock, show that LTROs have helped to increase the growth rate of real gross domestic product (GDP) and to raise the prospects of loan provisions for non-financial firms. Meanwhile, according to Ferrando et al.

(2015), small and medium enterprises (which are more reliant on local bank credit) are hit harder by the Euro Area’s credit crisis than large companies that can seek funding abroad. This result is more evident in the stressed countries (Spain, Italy, Greece, Portugal, and Ireland) than in the rest of the EMU countries.

Therefore, the existing literature that has already focused on the effects of ECB’s unconventional monetary policy on non-financial corporations is not only scarce but has not focused on how the different types of policy measures affected companies’ decisions on capital structure and capital allocation. This chapter will try to fill this gap.

2.3 Analytical framework

In order to analyze the possible effects of the ECB’s monetary policies on non-financial firms’ decisions, in this section, we will first review the literature on firms’ optimal choice of capital and then examine how interest rates could influence their decisions to allocate capital between investments and profit distribution – via dividends and buybacks, or a combination of the two.

2.3.2 Capital structure

One of the first studies on firms’ optimal choice of capital structure is the seminal paper by Modigliani and Miller (1958), who proposed what is known as the “leverage theorem.” According to this theorem, in a context of asymmetric information between companies and investors, a firm determines

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its leverage ratio based on the capital cost and access to finance. However, since then, many other theories have been proposed [Myers (1984), Kraus and Litzenberger (1973), or Merton (1974), to name a few]. Myers (1984) frame a company’s choice under the “pecking order” theory, which holds that firms prefer internal funds such as retained earnings to external financing, and debt to equity. Kraus and Litzenberger (1973) offer a competing view (the “trade-off” theory) which assumes that every company achieves an optimal capital structure (a “debt target”) at some point in time and trades off tax advantages from debt against refinancing cost risk. Other authors consider market conditions – including interest rates – as a variable that might influence companies’ decisions on their capital structure. Merton (1974), for example, examines from a theoretical perspective how changes in macroeconomic conditions influence companies on matters such as debt, while Barry et al. (2008) examine this subject, albeit empirically.

The theories mentioned above have different implications, not only regarding the reasons underlying the company’s decision to issue more debt but also about the effects that interest rate changes have on that decision. Although there is no consensus on the effect that interest rate changes have on capital structure decisions, in this chapter, we do not aim to explore the accuracy of those models. Our objective is to use them as a background to build up an econometric framework to examine how those changes may influence firms’

leverage decisions.

2.3.3 Capital spending, dividends, and buybacks

One of the ECB’s goals in implementing its extraordinary monetary policies was to boost investment. The underlying logic (a negative correlation between investments and interest rates) is prominent in a simple Keynesian IS-LM model where the interest rate and its coefficient of interest sensitivity determine investment:

𝐼 = 𝐼̅ + 𝑑𝑟

13 In the above equation, d>0 stands for the coefficient of interest sensitivity;

under normal economic conditions, falling interest rates should lead to higher investments and lift the aggregate demand. This relationship has mainly been examined in the literature from an empirical perspective, and its evolution in EMU countries from 1999 until the present is shown in Figure 2.1. This figure shows that it is not clear-cut in the Euro Area since it only suggests a limited relationship between investments and yields (the correlation over the period is not significant, although the fall in interest rates since 2014 coincided with a steady rise in investment in EMU countries).

Figure 2.1: Euro Area investment and 10- year European Union (EU) yield, quarterly data, 1999-2017

Source of data: Eurostat and European Central Bank data warehouse; EMU 10-year yield (right axis)

Nonetheless, the aim of this chapter goes beyond this relationship, since the goal is to analyze the effect of interest rates not only on investments but also on dividends and buybacks. To the best of our knowledge, this is the first attempt to examine how companies change their capital allocation between investments, buybacks, and dividends due to changes in interest rates. Below we present a simple analytical framework for understanding those relationships and the underlying assumptions behind them.

0.00 1.00 2.00 3.00 4.00 5.00 6.00

800,000 900,000 1,000,000 1,100,000 1,200,000 1,300,000 1,400,000

1999Q4 2000Q3 2001Q2 2002Q1 2002Q4 2003Q3 2004Q2 2005Q1 2005Q4 2006Q3 2007Q2 2008Q1 2008Q4 2009Q3 2010Q2 2011Q1 2011Q4 2012Q3 2013Q2 2014Q1 2014Q4 2015Q3 2016Q2 2017Q1

Euro Area Investment EU 10 Y

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Let us consider that a company, which has already taken on a debt obligation, needs to decide how to allocate its resources. Specifically, consider a company that needs to evaluate how much to invest in a particular project – noted as I – versus how much it should allocate to returning capital to shareholders – in the form of dividends or buybacks and noted as 𝑉– over a timeframe of two periods:

𝑍_𝑖 =^𝜋(𝐼)

1+𝑟+ 𝜌𝑉 (1)

𝑍_𝑖 is the added value to the company’s stock price, which the firm aims to maximize.

The firm has a budget constraint given by:

1 = I + 𝑉 (2)

This constraint means that the company has to divert all its resources towards an investment I in a particular project or towards paying its shareholders via dividends or buybacks – noted as V – or a combination of both (we are assuming that there are no other alternatives, for example, keeping the capital in cash).

The investment I will yield in the first period a profit of 𝜋(𝐼) – a convex, continuous function of I (let us assume that the company can allocate any portion it desires towards a particular project). This profit will need to be discounted with (1 + 𝑟) where r stands for the company’s cost of debt. For simplicity, we assume that r is the prevailing market interest rate (in other words, the company’s risk premium over the market is zero). Conversely, the company can allocate 𝑉 towards shareholders via dividends or buybacks.

This shareholder distribution has a positive and constant return 𝜌 . We then consider that profit distribution creates value for its shareholders because of its signaling mechanism about the positive prospects of the company’s future returns – especially if the company’s management considers its value to be

15 underestimated⁴. This positive correlation could be explained by agency costs, information asymmetries, and market irrationality (Fairchild, 2006). It is worth noting that while the empirical research has also shown a positive relationship between buybacks and stock prices (Gup and Nam, 2001), with regard to the relation between dividends and firm valuation (Black and Scholes, 1974), the empirical research is not conclusive. Using an international comparison, Denis and Osobov (2008) find scarce empirical evidence for a signaling effect for dividend-paying companies, Bernhardt et al. (2005) call into question the validity of signaling theories for dividends and Hussainey et al. (2011) support the positive relationship between dividends and share prices. In any case, for our model, we consider share buybacks and their more established positive relationships with a firm’s value to justify a company’s decision to allocate capital towards them instead of investing. In the econometric estimation, however, we use a broader term:

“shareholder yield”, which includes dividends, buybacks, and deleveraging.

With these methods, firms can return value to investors as a signaling mechanism.

Given these assumptions, we can solve the firm’s maximization problem to establish how a company distributes its capital in time zero between V and I, based on prevailing market interest rates. The Lagrangian equation is:

ℒ = ^𝜋(𝐼)

1+𝑟+ 𝜌𝑉 + λ(I + 𝑉 − 1) (3) The First order condition (FOC) for the investment is:

𝜋^′(𝐼) = −λ(1 + 𝑟) (4)

while the FOC for the shareholder distribution is:

4Dividends tend to be “stickier” since, even if market conditions are not good, companies are likely to keep them so as not to alarm investors. Conversely, when companies face a transitory gain, they tend to distribute their windfall through buybacks rather than raise dividends and thus lift expectations about future dividends. This could explain the rise in buybacks in recent years, mainly, although not solely, in the United States.

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−𝜌 = λ (5)

These two FOCs, before accounting for the λ budget constraint, lead to:

𝜋^′(𝐼)

(1+𝑟)= 𝜌 (6)

The solution shows that a company assesses a project based on two parameters: 𝜌 the company’s return to shareholders and 𝑟. Therefore, a company divides its resources between investments and shareholder distribution until the discounted marginal return on a given project is equal to the added value that a dividend or buyback has on a company’s stock price.

This framework might help us to understand how monetary policy changes could impact non-financial firms’ decisions on capital expenditure and shareholder yield⁵.

2.4 Data

Data have been gathered from companies’ financials provided by Bloomberg. We focus on non-financial firms listed in the leading stock exchanges from the four largest economies in the EMU: Germany (DAX), France (CAC40) Spain (IBEX35), and Italy (FTSE MIB)⁶. Explicitly, we gather quarterly data from a total of 62 non-financial firms (banks and insurance companies are excluded) registering a market capitalization of 2

5 To examine how these relationships work, we run simulations under different assumptions and investment functions. The results of these simulations suggest that under the baseline parameters, as r falls, companies tend to allocate more capital towards investment rather than on shareholders’

returns. However, as 𝜌 rises and interest rates fall, the tradeoff between investment and shareholder distribution tends to flatten. In other words, if the added value to shareholder is high enough mainly in a low interest rate environment, a further fall in the interest rate will not encourage firms to allocate more resources to investments rather than to shareholder distribution. Conversely, if 𝜌 is low, investment allocation is more likely to crowd out shareholder distribution as interest rates decline.

6 A good representation for the entire EMU, since their aggregate GDP accounts for roughly 75% of EMU’s GDP in 2017

17 trillion euros at the beginning of 2017 (which represents nearly a third of the total market capitalization of non-financial firms in the four leading stock exchanges). Therefore, our analysis focuses on large-cap companies since, although their number is not high, they represent a sizable portion of the market value of publicly traded non-financial firms in the EMU.

For our analysis, we use three main dependent variables: “CapEx-to-sales,”

“Debt-to-equity,” and “Shareholder yield”⁷, which capture capital spending, leverage, and capital distribution to shareholders respectively. Figures 2.2 and 2.3 show the high correlation between the first two variables’ behavior in the 62 companies included in the sample and in the four largest economies in the EMU (Germany, France, Spain and Italy) while a detailed description of them, together with the rest of the variables used in our analysis, is presented in Appendix A.

Figure 2.2: Capital formation in selected EMU countries and capital spending of firms in the sample, 2001-2016

Source of data: Bloomberg, Eurostat, and authors’ calculations. Data in millions of euros. Data set (left axis).

7 Because of data restrictions, we use the total amount that a company returns to its shareholders by distributing dividends, repurchase shares or paying back debt as a proxy of the “shareholder yield”.

2,500,000 2,700,000 2,900,000 3,100,000 3,300,000 3,500,000 3,700,000 3,900,000

100,000 120,000 140,000 160,000 180,000 200,000 220,000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Data Set

Selected EMU countries

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Figure 2.3: Private debt in selected EMU countries and total debt of firms in the sample, 2001-2016

Source of data: Bloomberg, Eurostat, and authors’ calculations. Data in millions of euros. Data set (left axis).

As for the independent variables that gauge monetary policies, we use changes to the ECB’s assets and the 3-month Euribor interest rate. The ECB’s assets are used because they show the different policy measures the ECB has employed over the years about changes to its balance sheet. This variable does not distinguish the different policy schemes such as LTRO, TLTRO, PSPP, ABSPP, CBPP3, and CSPP. These programmes have different targets, starting points, and budgets, and some have even wound down in recent years. However, all these policies aim to boost liquidity and reduce borrowing costs.

Moreover, since late 2014, the majority of the growth in the ECB’s assets is attributed to the PSPP. Therefore, we choose the changes to the ECB assets to show how these conventional and unconventional policies, without distinction, affect companies’ decisions. We then use the 3-month Euribor as

500,000 600,000 700,000 800,000 900,000 1,000,000 1,100,000 1,200,000 1,300,000

6,000,000 7,000,000 8,000,000 9,000,000 10,000,000 11,000,000 12,000,000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Debt of Private Sector in Selected EMU countries Debt in Data set

19 a proxy of the ECB’s direct impact on interest rates. We use this variable rather than the ECB’s deposit rate because it has a more direct connection to the interest rates faced by companies, and these two variables are highly correlated.

To produce a data matrix without missing values, we apply two complementary procedures: the technique of multiple imputation developed by King et al. (2001) (which permits the approximation of missing data and allows us to obtain better estimates) and the simultaneous nearest-neighbour predictors proposed by Fernández-Rodríguez et al. (1999) (which infers omitted values from patterns detected in other simultaneous time series).

2.5 Econometric estimation

Based on the theoretical framework laid out in Section 4, we estimate the econometric models for examining the role of monetary policy in determining firms’ capital spending, leverage, and shareholder payouts. Our panel data analysis relies on Blundell and Roulet (2013) who looked at 4,000 global companies and examined the impact of low interest rates – the direct result of the monetary policies of central banks including the Federal Reserve, the ECB and Bank of Japan in recent years – on their investments.

They conclude that, since capital spending depends on the cost of equity and uncertainty, low- interest rates and tax benefits incentivize long-term investment (because debt finance is cheap, companies have an incentive to borrow and carry out buybacks –also known as de-equitation).

2.5.1 Leverage

Two of the models most widely used in the literature to analyze the way a company decides on its capital structure are the tradeoff model of Kraus and Litzenberger (1973) and the pecking order model of Myers (1984). In the first model, a company raises its debt burden until it reaches a specific debt ratio

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target, and in the second, a company will first exhaust its internal funds (available cash) before raising funds from debt and equity. However, neither model analyzes the relationship between interest rates and the company’s decisions on debt as described in Section 3.1; nor do they examine the role of macroeconomic or monetary policy factors (such as QE programmes) on the capital structure of firms. Therefore, following Kühnhausen and Stiber (2014)⁸, in our model, we incorporate external variables that might influence a company’s decision on its debt-to-equity ratio (𝐿_𝑖,𝑡 is the dependent variable in the model, which measures the company’s debt burden or leverage):

𝐿_𝑖,𝑡 = 𝛼_𝑖,𝑡 + 𝛽₁∗ 𝑋_{𝑖,𝑡−1}+ 𝛽₂∗ 𝑌_𝑡−1+ 𝛽₃∗ 𝑍_𝑡−1 + 𝜀_𝑖,𝑡 (7)

As equation (7) shows, our model includes three prime independent variables. The first (X vector) corresponds to microeconomic variables that are attributed to each company and is also related to the tradeoff and pecking order models. The second (Y vector) comprises macroeconomic variables that may proxy the changes in the economy. Finally, the third (Z vector) includes variables that are directly or indirectly related to the ECB’s monetary policy and proxy supply-side developments⁹.

For our purposes, the monetary policy variables (Z vector) are the most important. They include the ECB’s asset levels – a proxy for the ECB’s asset purchase programmes and loans – and changes in the 3-month Euribor interest rate. Since the ECB added more funds to the economy and brought down interest rates to encourage companies to take on more loans, we should expect a negative correlation between companies’ leverage and interest rates and a positive correlation with the changes in the ECB’s assets. Regarding the microeconomic variables (X vector), three variables are included in the model: profitability (EBITDA-to-sales), growth in profits (growth in earnings per share or EPS), and WACC. We include the variables

8Their model is based on Rajan and Zingales (1995) and includes five macroeconomic factors: GDP per capita, the growth rate of GDP (in constant local currency), inflation rate, interest rate, and tax rate.

9 All independent variables, except WACC, lag the dependent variable by one period.

21 profitability and growth in profits since they play an essential role in determining the leverage of a company, as both Myers (1984) and Kraus and Litzenberger (1973)¹⁰ report. Additionally, the cost of capital (estimated by the Weighted Average Cost of Capital (WACC)) is a critical variable in this kind of model, and a negative relationship is to be expected between it and the leverage ratio. Finally, as regards the macroeconomic variables (Y vector), we have included the average inflation rate in the EMU because, since inflation depreciates the debt value in real terms, we should expect a positive relationship between this variable and leverage.

2.5.2 Capital spending and shareholder’s yield

To analyze the relationship between ECB’s monetary policy and the developments of capital spending and shareholder yields, we have adjusted the model described by Blundell and Roulet (2013), who conducted a panel data analysis and estimated two regressions (one for capital spending per sales and another for dividends and buybacks per sales). Therefore, we have also estimated two equations (an investment equation (8) and a shareholder yield equation (9)), but have adjusted their model by including variables that show how monetary policy affects capital expenditure and dividends/buybacks:

𝐶𝑖,𝑡= 𝛼𝑖,𝑡+ 𝛽1∗ 𝑖𝑡−1+ 𝛽2∗ 𝐸𝐶𝐵𝑡−1+ 𝛽3∗ 𝑆𝑡−1 + 𝛽4∗ 𝑃𝑡−1 + 𝛽5∗ 𝐸𝑖,𝑡−1 + 𝛽6∗ 𝑘𝑖,𝑡−1 + 𝜀𝑖,𝑡 (8)

𝑦_𝑖,𝑡= 𝛾_𝑖,𝑡+ 𝛽₇∗ 𝑖_𝑡−1+ 𝛽₈∗ 𝐸𝐶𝐵_𝑡−1+ 𝛽₉∗ 𝐸_{𝑖,𝑡−1}+ 𝛽₁₀∗ 𝑘_{𝑖,𝑡−1}+ 𝜗_𝑖,𝑡 (9)

In equation (8), the dependent variable is the company’s capital spending divided by sales (𝐶_𝑖,𝑡). The regression also includes the two main ECB policy variables – the cost of debt (it-1 which is proxied by 3-months Euribor rate) and the changes in the ECB’s assets (ECBt-1) – plus another four independent variables: the cost of capital (ki,t-1, measured by the WACC), changes in

10 The empirical evidence is also divided: Fama and French (2002) show that companies with higher profits tend to be less leveraged (thus correcting the pecking order model on this issue); whilst Frank and Goyal (2008) show the opposite.

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profits (Ei,t-1 proxied by EBITDA-to-Sales), the inflation rate in the EMU (Pt- 1), and the spread between long-term and short-term yields (St-1)¹¹.

By including the last two variables, we aim to test changes to the economy and market expectations that are directly linked to the ECB’s policies, while still including variables related to the ones Blundell and Roulet use in their analysis. In particular, inflation serves as a proxy for changes in demand and monetary policy. Nonetheless, the relationship between inflation and capital spending is not clear. On the one hand, higher inflationary pressures may lead the real returns (see Fama and Gibbons, 1982) on projects to be less profitable, but on the other, a rise in the rate of inflation might also indicate higher economic activity. As for the spread between long- and short-term rates, it is used as a proxy of economic conditions. According to Baumeister and Benati (2010), the compression of long-term bond spread may even impact GDP and inflation.

Furthermore, this compression tends to indicate a fall in the term premium.

The decline in the term premium could be due to lower expectations of either sudden inflation eruptions or lower interest rates in the future because of slower economic activity. In other words, a contracting spread, or the flattening of the yield curve, may correspond to companies reducing capital spending as economic activity deteriorates. Therefore, we would expect a positive relationship between capital expenditure and bond yield spread.

As stated above, our model includes an investment equation (8) and a shareholder yield equation (9) where the variables that may affect the shareholder yield (yi,t) are explored.

Like equation (8), equation (9) also includes the two main ECB policy variables – the cost of debt (it-1) and the changes in the ECB’s assets (ECBt- 1) – plus another two independent variables: the cost of capital (ki,t-1 measured by WACC) and changes in profits (Ei,t-1 proxied now by earnings per sale or

11 The spread between 10-year weighted average of sovereign bond yields of all EMU countries and 3-month Euribor rate.

23 EPS of each company). A positive relationship is expected for the former variable (if the cost of retaining a euro to invest relative to the cost of bonds rises, a company is better off repurchasing its shares – and reducing its relative rising cost of capital). Finally, regarding the latter variable, although Blundell and Roulet (2013) use an earnings yield in their model, we decided to use changes in EPS because it isolates the changes in a company’s fundamentals by not including the variations in its underlying stock price (which could shift based on changes to liquidity in the markets, supply and demand changes, and more). As for the expected relationship, even though there is no consensus in the literature¹², we still expect a rise in earnings to lead to higher returns to investors.

2.6 Empirical results

In this section, we first discuss the results from the panel data analysis applied to the leverage, the investment, and the shareholder yield regressions.

Specifically, we consider two basic panel regression methods: the fixed- effects (FE) method and the random effects (RE) model¹³. To determine the empirical relevance of each of the possible methods for our panel data, we test FE versus RE. We do so by using the Hausman test statistic to analyze the non-correlation between the unobserved effect and the regressors. This test indicates that the fixed effect estimators are more appropriate for all the timeframes in the leverage and the investment regressions. However, in the shareholder yield model, the Hausman test shows that the choice of method (FE or RE) depends on the subsample. Subsequently, we also present the results corresponding to a cross-country and a cross-sector analysis for the whole period in order to examine whether companies from different countries or industrial sectors react in different ways to the ECB’s policies.

12 According to Fama and French (2002), more profitable firms tend to have higher dividend payments. But Miller and Modigliani (1961) point out that rising profits do not necessarily lead to a rise in dividend payment – this will depend on other factors such as the payout ratio.

13 Estimations were also performed by the Arellano-Bond GMM approach, providing similar quantitative results.

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In the empirical estimation, we take into account the two substantial economic events which occurred during our sample period: (1) the global economic recession of 2008 and (2) the peak of the European debt crisis in 2011-2012, which may not only have played a substantial role in swaying European companies’ decisions, but may also have determined the ECB’s monetary policy. Based on the above, we introduce two breakpoints to capture these significant events: 2008Q1 (the tipping point for the global economic recession), and 2011Q3 (in order to examine not only whether the European debt crisis may have had an impact on the results, but also whether Mario Draghi’s leadership of the ECB had affected them). Therefore, we examined five different time frames: The first covers the sub-period 2000Q2- 2008Q1; the second spans from 2008Q2 to 2017Q4; the third ranges from 2000Q2 to 2011Q3; the fourth spans 2011Q4 and 2017Q4; and the last one covers the entire sample period from 2000Q2 to 2017Q4.

2.6.1 Panel unit root tests

A dependent stationary variable cannot be explained using non-stationary variables since the statistical properties of the former (mean, variance, autocorrelation, et cetera) remain constant over time, while those of the latter change. Therefore, to assess the statistical characteristics of our variables, we perform a variety of unit root tests in panel datasets. In particular, we use the Levin–Lin–Chu (2002), Harris–Tzavalis (1999), Breitung (2000), Im–

Pesaran–Shin (2003), and Fisher-type (Choi, 2001) tests. The results of these tests¹⁴ decisively reject the null hypothesis of a unit root for all the variables except for the ECB assets. Therefore, while the rest are found to be stationary in levels, the latter can be treated as the first-difference stationery. So, in the different empirical estimations, the variable ECB assets will be transformed into a stationary variable by differencing it.

14 They are not shown in this paper to save space.

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2.6.2 Leverage: Empirical results

The results regarding the main drivers of the leverage ratio are presented in Table 2.1. As can be seen, we report the results obtained using the FE model since it is the relevant one in all cases.

Table 2.1: Results of panel analysis for debt-to-equity

OLS Estimates of the Effect of the ECB’s policies on Leverage Dependent variable: Debt-to-equity

2001Q2- 2011Q3

2011Q4- 2017Q4

2001Q2- 2008Q1

2008Q2- 2017Q4

2001Q2- 2017Q4

(1) (2) (3) (4) (5)

D(ECB Assets (t-1)) 1.22** 8.56*** 1.25** 65.4** 0.1711***

3 Mo Yld (t-1) -1.174*** -2.495** -0.655*** -3.968*** -3.459***

EPS (t-1) -2.129*** -1.213*** -1.872*** -2.804*** -2.437***

WACC -7.547*** -4.396*** -6.506** -3.802*** -8.214***

EBITDA to Revenue (t-1) 0.028*** 0.058*** 0.120** 0.542*** 0.159***

EU inflation (t-1) 0.947*** 1.768*** 7.034** 1.086*** 4.300***

Constant 162.11*** 130.81*** 149.38*** 130.88*** 154.77***

Statistics

R-squared (overall) 81.4% 82.7% 83.3% 66.7% 75.5%

F-statistic 49.28*** 22.54*** 51.50*** 54.71*** 53.40***

Total Obs. 3160 1240 2044 2480 4462

Cross sections 62 62 62 62 62

Hausman Test (Chi-Sq Stat.) 34.91*** 47.35*** 32.12*** 79.21*** 36.01***

RE/FE FE FE FE FE FE

This table shows the results of estimating an equation for a balanced panel of 62 publicly traded non-financial firms. *, **, *** indicate statistical significance at the 10, 5% and 1% levels respectively.

Results in Table 2.1 indicate that interest rates and changes to the ECB’s balance sheet have a positive and significant impact on companies’ leverage.

For the entire period (column 5), a one-percentage-point fall in the 3-months Euribor tends to lift the debt-to-equity ratio, on average, by 3.46 percentage points. Moreover, for every 1 trillion euros the ECB adds to its balance sheet via the various LTRO and QE programmes, companies are likely to raise

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their debt ratio, on average, by 0.17 percentage points. A closer examination of the results also reveals that the ECB’s policies have a stronger marginal effect on companies’ debt-to-equity ratio after 2011Q3 (column 2) and 2008Q1 (column 4). Specifically, the 3-months Euribor coefficients in column 4 (-3.968) and column 2 (-2.495) are much lower than the coefficients in column 3 (-0.655) and column 1 (-1.174). As for changes in the ECB’s assets, the coefficients are much higher in columns 4 and 2 than in columns 1 and 3. The inflation rate, which is another variable that is indirectly affected by monetary policy, also presents positive and significant coefficients across different time frames. Finally, the fit of the overall regressions is satisfactory as measured by the R² values, which range from 66.7% to 83.3% for the various time samples.

2.6.3 Capital spending: Empirical results

The results corresponding to the investment equation (8) are presented in Table 2.2. Once again, the FE model is found to be the relevant one in all sample periods under consideration. It can be observed that the ECB’s policies (both changes in interest rates and balance sheet assets) have a significant and stimulating impact on a company’s capital spending across the different periods under study. In particular, from 2001 to 2017 (column 5) for every 1 trillion euros buildup in the ECB’s assets, the capital-spending- to-sales ratio rises, on average, by 2.98 percentage points. As for interest rates, a decline of one percentage point in the 3-months Euribor tends to raise the CapEx-to-sales ratio, on average, by 1.5 percentage points.